Slope stability analysis: a support vector machine approach

被引:182
作者
Samui, Pijush [1 ]
机构
[1] Indian Inst Sci, Dept Civil Engn, Bangalore 560012, Karnataka, India
来源
ENVIRONMENTAL GEOLOGY | 2008年 / 56卷 / 02期
关键词
Artificial Neural Network; Slope stability; Support Vector Machine;
D O I
10.1007/s00254-007-1161-4
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Artificial Neural Network (ANN) such as backpropagation learning algorithm has been successfully used in slope stability problem. However, generalization ability of conventional ANN has some limitations. For this reason, Support Vector Machine (SVM) which is firmly based on the theory of statistical learning has been used in slope stability problem. An interesting property of this approach is that it is an approximate implementation of a structural risk minimization (SRM) induction principle that aims at minimizing a bound on the generalization error of a model, rather than minimizing only the mean square error over the data set. In this study, SVM predicts the factor of safety that has been modeled as a regression problem and stability status that has been modeled as a classification problem. For factor of safety prediction, SVM model gives better result than previously published result of ANN model. In case of stability status, SVM gives an accuracy of 85.71%.
引用
收藏
页码:255 / 267
页数:13
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